Please make sure to check out the installation instructions and data before participating. There might be no sufficient internet connection at the venue.

Instructions and data can be found here:

Machine learning requires experimenting with a wide range of datasets, data preparation steps, and algorithms to build a model that maximizes some target metric. Once you have built a model, you also need to deploy it to a production system, monitor its performance, and continuously retrain it on new data and compare with alternative models. A possible solution to managing this complexity is offered by MLFlow. MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

This tutorial showcases how you can use MLflow end-to-end to:

  • Train models and keep track of experiments with MLflow Tracking
  • Package the code that trains the model in a reusable and reproducible model format with MLFlow Projects
  • Deploy the model into a HTTP server that will enable you to score predictions with MLFlow Models

Tobias Sterbak

Affiliation: Freelancer @ depends-on-the-defintion

Data Scientist | Deep Learning Practitioner | Mathematician

You can find me on Twitter @tobias_sterbak and blogging on

visit the speaker at: TwitterGithubHomepage